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Below is my code , which is running fine but after a long processing it show me the run time error

//FlannBasedMatcher matcher;
std::vector< DMatch > matches;
double max_dist = 0; 
double min_dist = 100;

Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("FlannBased");
Ptr<DescriptorExtractor> extractor = new SurfDescriptorExtractor();

//SurfDescriptorExtractor extractor;
SurfFeatureDetector detector(500);
std::vector<KeyPoint> keypoints;
int dictionarySize = 1500;
TermCriteria tc(CV_TERMCRIT_ITER, 10, 0.001);
int retries = 1;
int flags = KMEANS_PP_CENTERS;
BOWKMeansTrainer bow(dictionarySize, tc, retries, flags);
BOWImgDescriptorExtractor dextract(extractor,matcher);

Mat img_keypoints_1;
Mat descriptors_1;
string YourImagesDirectory="D:\\Cars\\";
vector<string> files=listFilesInDirectory(YourImagesDirectory+"*.jpg");
//Load NOT cars!
string YourImagesDirectory_2="D:\\not_cars\\";
vector<string> files_no=listFilesInDirectory(YourImagesDirectory_2+"*.jpg");

// Initialize constant values
const int nb_cars = files.size();
const int not_cars = files_no.size();
const int num_img = nb_cars + not_cars; // Get the number of images
 const int image_area = 40*30;
// Initialize your training set.
cv::Mat training_mat(num_img,image_area,CV_32FC1);
cv::Mat labels(num_img,1,CV_32FC1);
cv::Mat tmp_dst( 500, 450, CV_8UC1 ); // to the right size for resize
// Set temp matrices
cv::Mat tmp_img;

std::vector<string> all_names;

all_names.assign(files.begin(),files.end());
all_names.insert(all_names.end(), files_no.begin(), files_no.end());

// Load image and add them to the training set
int count = 0;
vector<string>::const_iterator i;
string Dir;
for (i = all_names.begin(); i != all_names.end(); ++i)
{
    Dir=( (count < files.size() ) ? YourImagesDirectory : YourImagesDirectory_2);

    tmp_img = cv::imread( Dir +*i, 0 );

    resize( tmp_img, tmp_dst, tmp_dst.size() );

    Mat row_img = tmp_dst; // get a one line image.

    detector.detect( row_img, keypoints);

    extractor->compute( row_img, keypoints, descriptors_1);

    bow.add(descriptors_1);

    ++count;
}

int count_2=0;
vector<string>::const_iterator k;
Mat vocabulary = bow.cluster();
dextract.setVocabulary(vocabulary);
for (k = all_names.begin(); k != all_names.end(); ++k)
{
    Dir=( (count_2 < files.size() ) ? YourImagesDirectory : YourImagesDirectory_2);

    tmp_img = cv::imread( Dir +*k, 0 );

    resize( tmp_img, tmp_dst, tmp_dst.size() );

    Mat row_img = tmp_dst; // get a one line image.

    detector.detect( row_img, keypoints);

    dextract.compute( row_img, keypoints, descriptors_1);

    training_mat.push_back(descriptors_1);

    labels.at< float >(count_2, 0) = (count_2<nb_cars)?1:-1; // 1 for car, -1 otherwise*/

    ++count_2;
}

// Train your SVM
CvSVMParams Params;
Params.svm_type=CvSVM::C_SVC;
Params.kernel_type=CvSVM::LINEAR;
Params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER, 100, 1e-6);
Params.gamma=3;
CvSVM svm;
svm.train(training_mat,labels,cv::Mat(),cv::Mat(),Params);
svm.save("trainsvm.txt");

Error :

This code is implemented with the help of pseudo code given by @Mathieu Barnachon here

and how do i know that now my data is trained according to my need and now i can implement on my real time app to detect objects from video

Below is my code , which is running fine but after a long processing it show me the run time error

//FlannBasedMatcher matcher;
std::vector< DMatch > matches;
double max_dist = 0; 
double min_dist = 100;

Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("FlannBased");
Ptr<DescriptorExtractor> extractor = new SurfDescriptorExtractor();

//SurfDescriptorExtractor extractor;
SurfFeatureDetector detector(500);
std::vector<KeyPoint> keypoints;
int dictionarySize = 1500;
TermCriteria tc(CV_TERMCRIT_ITER, 10, 0.001);
int retries = 1;
int flags = KMEANS_PP_CENTERS;
BOWKMeansTrainer bow(dictionarySize, tc, retries, flags);
BOWImgDescriptorExtractor dextract(extractor,matcher);

Mat img_keypoints_1;
Mat descriptors_1;
string YourImagesDirectory="D:\\Cars\\";
vector<string> files=listFilesInDirectory(YourImagesDirectory+"*.jpg");
//Load NOT cars!
string YourImagesDirectory_2="D:\\not_cars\\";
vector<string> files_no=listFilesInDirectory(YourImagesDirectory_2+"*.jpg");

// Initialize constant values
const int nb_cars = files.size();
const int not_cars = files_no.size();
const int num_img = nb_cars + not_cars; // Get the number of images
 const int image_area = 40*30;
// Initialize your training set.
cv::Mat training_mat(num_img,image_area,CV_32FC1);
cv::Mat labels(num_img,1,CV_32FC1);
cv::Mat tmp_dst( 500, 450, CV_8UC1 ); // to the right size for resize
// Set temp matrices
cv::Mat tmp_img;

std::vector<string> all_names;

all_names.assign(files.begin(),files.end());
all_names.insert(all_names.end(), files_no.begin(), files_no.end());

// Load image and add them to the training set
int count = 0;
vector<string>::const_iterator i;
string Dir;
for (i = all_names.begin(); i != all_names.end(); ++i)
{
    Dir=( (count < files.size() ) ? YourImagesDirectory : YourImagesDirectory_2);

    tmp_img = cv::imread( Dir +*i, 0 );

    resize( tmp_img, tmp_dst, tmp_dst.size() );

    Mat row_img = tmp_dst; // get a one line image.

    detector.detect( row_img, keypoints);

    extractor->compute( row_img, keypoints, descriptors_1);

    bow.add(descriptors_1);

    ++count;
}

int count_2=0;
vector<string>::const_iterator k;
Mat vocabulary = bow.cluster();
dextract.setVocabulary(vocabulary);
for (k = all_names.begin(); k != all_names.end(); ++k)
{
    Dir=( (count_2 < files.size() ) ? YourImagesDirectory : YourImagesDirectory_2);

    tmp_img = cv::imread( Dir +*k, 0 );

    resize( tmp_img, tmp_dst, tmp_dst.size() );

    Mat row_img = tmp_dst; // get a one line image.

    detector.detect( row_img, keypoints);

    dextract.compute( row_img, keypoints, descriptors_1);

    training_mat.push_back(descriptors_1);

    labels.at< float >(count_2, 0) = (count_2<nb_cars)?1:-1; // 1 for car, -1 otherwise*/

    ++count_2;
}

// Train your SVM
CvSVMParams Params;
Params.svm_type=CvSVM::C_SVC;
Params.kernel_type=CvSVM::LINEAR;
Params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER, 100, 1e-6);
Params.gamma=3;
CvSVM svm;
svm.train(training_mat,labels,cv::Mat(),cv::Mat(),Params);
svm.save("trainsvm.txt");

Error :

This code is implemented with the help of pseudo code given by @Mathieu Barnachon here

and how do i know that now my data is trained according to my need and now i can implement on my real time app to detect objects from video

Below is my code , which is running fine but after a long processing it show me the run time error

//FlannBasedMatcher matcher;
std::vector< DMatch > matches;
double max_dist = 0; 
double min_dist = 100;

Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("FlannBased");
Ptr<DescriptorExtractor> extractor = new SurfDescriptorExtractor();

//SurfDescriptorExtractor extractor;
SurfFeatureDetector detector(500);
std::vector<KeyPoint> keypoints;
int dictionarySize = 1500;
TermCriteria tc(CV_TERMCRIT_ITER, 10, 0.001);
int retries = 1;
int flags = KMEANS_PP_CENTERS;
BOWKMeansTrainer bow(dictionarySize, tc, retries, flags);
BOWImgDescriptorExtractor dextract(extractor,matcher);

Mat img_keypoints_1;
Mat descriptors_1;
string YourImagesDirectory="D:\\Cars\\";
vector<string> files=listFilesInDirectory(YourImagesDirectory+"*.jpg");
//Load NOT cars!
string YourImagesDirectory_2="D:\\not_cars\\";
vector<string> files_no=listFilesInDirectory(YourImagesDirectory_2+"*.jpg");

// Initialize constant values
const int nb_cars = files.size();
const int not_cars = files_no.size();
const int num_img = nb_cars + not_cars; // Get the number of images
 const int image_area = 40*30;
// Initialize your training set.
cv::Mat training_mat(num_img,image_area,CV_32FC1);
cv::Mat labels(num_img,1,CV_32FC1);
cv::Mat tmp_dst( 500, 450, CV_8UC1 ); // to the right size for resize
// Set temp matrices
cv::Mat tmp_img;

std::vector<string> all_names;

all_names.assign(files.begin(),files.end());
all_names.insert(all_names.end(), files_no.begin(), files_no.end());

// Load image and add them to the training set
int count = 0;
vector<string>::const_iterator i;
string Dir;
for (i = all_names.begin(); i != all_names.end(); ++i)
{
    Dir=( (count < files.size() ) ? YourImagesDirectory : YourImagesDirectory_2);

    tmp_img = cv::imread( Dir +*i, 0 );

    resize( tmp_img, tmp_dst, tmp_dst.size() );

    Mat row_img = tmp_dst; // get a one line image.

    detector.detect( row_img, keypoints);

    extractor->compute( row_img, keypoints, descriptors_1);

    bow.add(descriptors_1);

    ++count;
}

int count_2=0;
vector<string>::const_iterator k;
Mat vocabulary = bow.cluster();
dextract.setVocabulary(vocabulary);
for (k = all_names.begin(); k != all_names.end(); ++k)
{
    Dir=( (count_2 < files.size() ) ? YourImagesDirectory : YourImagesDirectory_2);

    tmp_img = cv::imread( Dir +*k, 0 );

    resize( tmp_img, tmp_dst, tmp_dst.size() );

    Mat row_img = tmp_dst; // get a one line image.

    detector.detect( row_img, keypoints);

    dextract.compute( row_img, keypoints, descriptors_1);

    training_mat.push_back(descriptors_1);

    labels.at< float >(count_2, 0) = (count_2<nb_cars)?1:-1; // 1 for car, -1 otherwise*/

    ++count_2;
}

// Train your SVM
CvSVMParams Params;
Params.svm_type=CvSVM::C_SVC;
Params.kernel_type=CvSVM::LINEAR;
Params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER, 100, 1e-6);
Params.gamma=3;
CvSVM svm;
svm.train(training_mat,labels,cv::Mat(),cv::Mat(),Params);
svm.save("trainsvm.txt");

Error :

This code is implemented with the help of pseudo code given by @Mathieu Barnachon here

and how do i know that now my data is trained according to my need and now i can implement on my real time app to detect objects from video

Below is my code , which is running fine but after a long processing it show me the run time error

//FlannBasedMatcher matcher;
std::vector< DMatch > matches;
double max_dist = 0; 
double min_dist = 100;

Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("FlannBased");
Ptr<DescriptorExtractor> extractor = new SurfDescriptorExtractor();

//SurfDescriptorExtractor extractor;
SurfFeatureDetector detector(500);
std::vector<KeyPoint> keypoints;
int dictionarySize = 1500;
TermCriteria tc(CV_TERMCRIT_ITER, 10, 0.001);
int retries = 1;
int flags = KMEANS_PP_CENTERS;
BOWKMeansTrainer bow(dictionarySize, tc, retries, flags);
BOWImgDescriptorExtractor dextract(extractor,matcher);

Mat img_keypoints_1;
Mat descriptors_1;
string YourImagesDirectory="D:\\Cars\\";
vector<string> files=listFilesInDirectory(YourImagesDirectory+"*.jpg");
//Load NOT cars!
string YourImagesDirectory_2="D:\\not_cars\\";
vector<string> files_no=listFilesInDirectory(YourImagesDirectory_2+"*.jpg");

// Initialize constant values
const int nb_cars = files.size();
const int not_cars = files_no.size();
const int num_img = nb_cars + not_cars; // Get the number of images
 const int image_area = 40*30;
// Initialize your training set.
cv::Mat training_mat(num_img,image_area,CV_32FC1);
cv::Mat labels(num_img,1,CV_32FC1);
cv::Mat tmp_dst( 500, 450, CV_8UC1 ); // to the right size for resize
// Set temp matrices
cv::Mat tmp_img;

std::vector<string> all_names;

all_names.assign(files.begin(),files.end());
all_names.insert(all_names.end(), files_no.begin(), files_no.end());

// Load image and add them to the training set
int count = 0;
vector<string>::const_iterator i;
string Dir;
for (i = all_names.begin(); i != all_names.end(); ++i)
{
    Dir=( (count < files.size() ) ? YourImagesDirectory : YourImagesDirectory_2);

    tmp_img = cv::imread( Dir +*i, 0 );

    resize( tmp_img, tmp_dst, tmp_dst.size() );

    Mat row_img = tmp_dst; // get a one line image.

    detector.detect( row_img, keypoints);

    extractor->compute( row_img, keypoints, descriptors_1);

    bow.add(descriptors_1);

    ++count;
}

int count_2=0;
vector<string>::const_iterator k;
Mat vocabulary = bow.cluster();
dextract.setVocabulary(vocabulary);
for (k = all_names.begin(); k != all_names.end(); ++k)
{
    Dir=( (count_2 < files.size() ) ? YourImagesDirectory : YourImagesDirectory_2);

    tmp_img = cv::imread( Dir +*k, 0 );

    resize( tmp_img, tmp_dst, tmp_dst.size() );

    Mat row_img = tmp_dst; // get a one line image.

    detector.detect( row_img, keypoints);

    dextract.compute( row_img, keypoints, descriptors_1);

    training_mat.push_back(descriptors_1);

    labels.at< float >(count_2, 0) = (count_2<nb_cars)?1:-1; // 1 for car, -1 otherwise*/

    ++count_2;
}

// Train your SVM
CvSVMParams Params;
Params.svm_type=CvSVM::C_SVC;
Params.kernel_type=CvSVM::LINEAR;
Params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER, 100, 1e-6);
Params.gamma=3;
CvSVM svm;
svm.train(training_mat,labels,cv::Mat(),cv::Mat(),Params);
svm.save("trainsvm.txt");

Error :

This code is implemented with the help of pseudo code given by @Mathieu Barnachon here

and how do i know that now my data is trained according to my need and now i can implement on my real time app to detect objects from video

@SR how i know where i am doing mistake its runtime logical error , if i knw where i did mistake than why i post the question here , and i post the small peace of code here and its clear from the error than i post the second part and so on ? i think i did the coding on my own and i am here for help not for coding , thats why i mention the error here , not demanding for codecode , and your point of view on long question is waste of time here , so than we can only question here if it is of one line ? i think my question is short and clear , i just want to know the error and that's it

Below is my code , which is running fine but after a long processing it show me the run time error

//FlannBasedMatcher matcher;
std::vector< DMatch > matches;
double max_dist = 0; 
double min_dist = 100;

Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("FlannBased");
Ptr<DescriptorExtractor> extractor = new SurfDescriptorExtractor();

//SurfDescriptorExtractor extractor;
SurfFeatureDetector detector(500);
std::vector<KeyPoint> keypoints;
int dictionarySize = 1500;
TermCriteria tc(CV_TERMCRIT_ITER, 10, 0.001);
int retries = 1;
int flags = KMEANS_PP_CENTERS;
BOWKMeansTrainer bow(dictionarySize, tc, retries, flags);
BOWImgDescriptorExtractor dextract(extractor,matcher);

Mat img_keypoints_1;
Mat descriptors_1;
string YourImagesDirectory="D:\\Cars\\";
vector<string> files=listFilesInDirectory(YourImagesDirectory+"*.jpg");
//Load NOT cars!
string YourImagesDirectory_2="D:\\not_cars\\";
vector<string> files_no=listFilesInDirectory(YourImagesDirectory_2+"*.jpg");

// Initialize constant values
const int nb_cars = files.size();
const int not_cars = files_no.size();
const int num_img = nb_cars + not_cars; // Get the number of images
 const int image_area = 40*30;
// Initialize your training set.
cv::Mat training_mat(num_img,image_area,CV_32FC1);
cv::Mat labels(num_img,1,CV_32FC1);
cv::Mat tmp_dst( 500, 450, CV_8UC1 ); // to the right size for resize
// Set temp matrices
cv::Mat tmp_img;
resize    
std::vector<string> all_names;

all_names.assign(files.begin(),files.end());
all_names.insert(all_names.end(), files_no.begin(), files_no.end());

// Load image and add them to the training set
int count = 0;
vector<string>::const_iterator i;
string Dir;
for (i = all_names.begin(); i != all_names.end(); ++i)
{
    Dir=( (count < files.size() ) ? YourImagesDirectory : YourImagesDirectory_2);

    tmp_img = cv::imread( Dir +*i, 0 );

    resize( tmp_img, tmp_dst, tmp_dst.size() );

    Mat row_img = tmp_dst; // get a one line image.

    detector.detect( row_img, keypoints);

    extractor->compute( row_img, keypoints, descriptors_1);

    bow.add(descriptors_1);

    ++count;
}

int count_2=0;
vector<string>::const_iterator k;
Mat vocabulary = bow.cluster();
dextract.setVocabulary(vocabulary);
for (k = all_names.begin(); k != all_names.end(); ++k)
{
    Dir=( (count_2 < files.size() ) ? YourImagesDirectory : YourImagesDirectory_2);

    tmp_img = cv::imread( Dir +*k, 0 );

    resize( tmp_img, tmp_dst, tmp_dst.size() );

    Mat row_img = tmp_dst; // get a one line image.

    detector.detect( row_img, keypoints);

    dextract.compute( row_img, keypoints, descriptors_1);

    training_mat.push_back(descriptors_1);

    labels.at< float >(count_2, 0) = (count_2<nb_cars)?1:-1; // 1 for car, -1 otherwise*/

    ++count_2;
}

// Train your SVM
CvSVMParams Params;
Params.svm_type=CvSVM::C_SVC;
Params.kernel_type=CvSVM::LINEAR;
Params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER, 100, 1e-6);
Params.gamma=3;
CvSVM svm;
svm.train(training_mat,labels,cv::Mat(),cv::Mat(),Params);
svm.save("trainsvm.txt");

Error :

This code is implemented with the help of pseudo code given by @Mathieu Barnachon here

and how do i know that now my data is trained according to my need and now i can implement on my real time app to detect objects from video

@SR how i know where i am doing mistake its runtime logical error , if i knw where i did mistake than why i post the question here , and i post the small peace of code here and its clear from the error than i post the second part and so on ? i think i did the coding on my own and i am here for help not for coding , thats why i mention the error here , not demanding for code , and your point of view on long question is waste of time here , so than we can only question here if it is of one line ? i think my question is short and clear , i just want to know the error and that's it

Below is my code , which is running fine but after a long processing it show me the run time error

//FlannBasedMatcher matcher;
std::vector< DMatch > matches;
double max_dist = 0; 
double min_dist = 100;

Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("FlannBased");
Ptr<DescriptorExtractor> extractor = new SurfDescriptorExtractor();

//SurfDescriptorExtractor extractor;
SurfFeatureDetector detector(500);
std::vector<KeyPoint> keypoints;
int dictionarySize = 1500;
TermCriteria tc(CV_TERMCRIT_ITER, 10, 0.001);
int retries = 1;
int flags = KMEANS_PP_CENTERS;
BOWKMeansTrainer bow(dictionarySize, tc, retries, flags);
BOWImgDescriptorExtractor dextract(extractor,matcher);

Mat img_keypoints_1;
Mat descriptors_1;
string YourImagesDirectory="D:\\Cars\\";
vector<string> files=listFilesInDirectory(YourImagesDirectory+"*.jpg");
//Load NOT cars!
string YourImagesDirectory_2="D:\\not_cars\\";
vector<string> files_no=listFilesInDirectory(YourImagesDirectory_2+"*.jpg");

// Initialize constant values
const int nb_cars = files.size();
const int not_cars = files_no.size();
const int num_img = nb_cars + not_cars; // Get the number of images
// Initialize your training set.
cv::Mat training_mat(num_img,image_area,CV_32FC1);
cv::Mat labels(num_img,1,CV_32FC1);
cv::Mat tmp_dst( 500, 450, CV_8UC1 ); // to the right size for resize    
std::vector<string> all_names;

all_names.assign(files.begin(),files.end());
all_names.insert(all_names.end(), files_no.begin(), files_no.end());

// Load image and add them to the training set
int count = 0;
vector<string>::const_iterator i;
string Dir;
for (i = all_names.begin(); i != all_names.end(); ++i)
{
    Dir=( (count < files.size() ) ? YourImagesDirectory : YourImagesDirectory_2);

    tmp_img = cv::imread( Dir +*i, 0 );

    resize( tmp_img, tmp_dst, tmp_dst.size() );

    Mat row_img = tmp_dst; // get a one line image.

    detector.detect( row_img, keypoints);

    extractor->compute( row_img, keypoints, descriptors_1);

    bow.add(descriptors_1);

    ++count;
}

int count_2=0;
vector<string>::const_iterator k;
Mat vocabulary = bow.cluster();
dextract.setVocabulary(vocabulary);
for (k = all_names.begin(); k != all_names.end(); ++k)
{
    Dir=( (count_2 < files.size() ) ? YourImagesDirectory : YourImagesDirectory_2);

    tmp_img = cv::imread( Dir +*k, 0 );

    resize( tmp_img, tmp_dst, tmp_dst.size() );

    Mat row_img = tmp_dst; // get a one line image.

    detector.detect( row_img, keypoints);

    dextract.compute( row_img, keypoints, descriptors_1);

    training_mat.push_back(descriptors_1);

    labels.at< float >(count_2, 0) = (count_2<nb_cars)?1:-1; // 1 for car, -1 otherwise*/

    ++count_2;
}

Error :

This code is implemented with the help of pseudo code given by @Mathieu Barnachon here

and how do i know that now my data is trained according to my need and now i can implement on my real time app to detect objects from video

@SR how i know where i am doing mistake its runtime logical error , if i knw where i did mistake than why i post the question here , and i post the small peace of code here and its clear from the error than i post the second part and so on ? i think i did the coding on my own and i am here for help not for coding , thats why i mention the error here , not demanding for code , and your point of view on long question is waste of time here , so than we can only question here if it is of one line ? i think my question is short and clear , i just want to know the error and that's it

Below is my code , which is running fine but after a long processing it show me the run time error

//FlannBasedMatcher matcher;
std::vector< DMatch > matches;
double max_dist = 0; 
double min_dist = 100;

Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("FlannBased");
Ptr<DescriptorExtractor> extractor = new SurfDescriptorExtractor();

//SurfDescriptorExtractor extractor;
SurfFeatureDetector detector(500);
std::vector<KeyPoint> keypoints;
int dictionarySize = 1500;
TermCriteria tc(CV_TERMCRIT_ITER, 10, 0.001);
int retries = 1;
int flags = KMEANS_PP_CENTERS;
BOWKMeansTrainer bow(dictionarySize, tc, retries, flags);
BOWImgDescriptorExtractor dextract(extractor,matcher);

Mat img_keypoints_1;
Mat descriptors_1;
string YourImagesDirectory="D:\\Cars\\";
vector<string> files=listFilesInDirectory(YourImagesDirectory+"*.jpg");
//Load NOT cars!
string YourImagesDirectory_2="D:\\not_cars\\";
vector<string> files_no=listFilesInDirectory(YourImagesDirectory_2+"*.jpg");

// Initialize constant values
const int nb_cars = files.size();
const int not_cars = files_no.size();
const int num_img = nb_cars + not_cars; // Get the number of images
// Initialize your training set.
cv::Mat training_mat(num_img,image_area,CV_32FC1);
cv::Mat labels(num_img,1,CV_32FC1);
cv::Mat tmp_dst( 500, 450, CV_8UC1 ); // to the right size for resize    
std::vector<string> all_names;

all_names.assign(files.begin(),files.end());
all_names.insert(all_names.end(), files_no.begin(), files_no.end());

// Load image and add them to the training set
int count = 0;
vector<string>::const_iterator i;
string Dir;
for (i = all_names.begin(); i != all_names.end(); ++i)
{
    Dir=( (count < files.size() ) ? YourImagesDirectory : YourImagesDirectory_2);

    tmp_img = cv::imread( Dir +*i, 0 );

    resize( tmp_img, tmp_dst, tmp_dst.size() );

    Mat row_img = tmp_dst; // get a one line image.

    detector.detect( row_img, keypoints);

    extractor->compute( row_img, keypoints, descriptors_1);

    bow.add(descriptors_1);

    ++count;
}

int count_2=0;
vector<string>::const_iterator k;
Mat vocabulary = bow.cluster();
dextract.setVocabulary(vocabulary);
for (k = all_names.begin(); k != all_names.end(); ++k)
{
    Dir=( (count_2 < files.size() ) ? YourImagesDirectory : YourImagesDirectory_2);

    tmp_img = cv::imread( Dir +*k, 0 );

    resize( tmp_img, tmp_dst, tmp_dst.size() );

    Mat row_img = tmp_dst; // get a one line image.

    detector.detect( row_img, keypoints);

    dextract.compute( row_img, keypoints, descriptors_1);

    training_mat.push_back(descriptors_1);

    labels.at< float >(count_2, 0) = (count_2<nb_cars)?1:-1; // 1 for car, -1 otherwise*/

    ++count_2;
}

Error :

This code is implemented with the help of pseudo code given by @Mathieu Barnachon here

and how do i know that now my data is trained according to my need and now i can implement on my real time app to detect objects from video

@SR how i know where i am doing mistake its runtime logical error , if i knw where i did mistake than why i post the question here , and i post the small peace of code here and its clear from the error than i post the second part and so on ? i think i did the coding on my own and i am here for help not for coding , thats why i mention the error here , not demanding for code , and your point of view on long question is waste of time here , so than we can only question here if it is of one line ? i think my question is short and clear , i just want to know the error and that's it

Below is my code , which is running fine but after a long processing it show me the run time error

//FlannBasedMatcher matcher;
std::vector< DMatch > matches;
double max_dist = 0; 
double min_dist = 100;

Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("FlannBased");
Ptr<DescriptorExtractor> extractor = new SurfDescriptorExtractor();

//SurfDescriptorExtractor extractor;
SurfFeatureDetector detector(500);
std::vector<KeyPoint> keypoints;
int dictionarySize = 1500;
TermCriteria tc(CV_TERMCRIT_ITER, 10, 0.001);
int retries = 1;
int flags = KMEANS_PP_CENTERS;
BOWKMeansTrainer bow(dictionarySize, tc, retries, flags);
BOWImgDescriptorExtractor dextract(extractor,matcher);
// Initialize your training set.
cv::Mat training_mat(num_img,image_area,CV_32FC1);
cv::Mat labels(num_img,1,CV_32FC1);
cv::Mat tmp_dst( 500, 450, CV_8UC1 ); // to the right size for resize    
std::vector<string> all_names;

all_names.assign(files.begin(),files.end());
all_names.insert(all_names.end(), files_no.begin(), files_no.end());

// Load image and add them to the training set
int count = 0;
vector<string>::const_iterator i;
string Dir;
for (i = all_names.begin(); i != all_names.end(); ++i)
{
    Dir=( (count < files.size() ) ? YourImagesDirectory : YourImagesDirectory_2);

    tmp_img = cv::imread( Dir +*i, 0 );

    resize( tmp_img, tmp_dst, tmp_dst.size() );

    Mat row_img = tmp_dst; // get a one line image.
tmp_dst;

    detector.detect( row_img, keypoints);

    extractor->compute( row_img, keypoints, descriptors_1);

    bow.add(descriptors_1);

    ++count;
}

int count_2=0;
vector<string>::const_iterator k;
Mat vocabulary = bow.cluster();
dextract.setVocabulary(vocabulary);
for (k = all_names.begin(); k != all_names.end(); ++k)
{
    Dir=( (count_2 < files.size() ) ? YourImagesDirectory : YourImagesDirectory_2);

    tmp_img = cv::imread( Dir +*k, 0 );

    resize( tmp_img, tmp_dst, tmp_dst.size() );

    Mat row_img = tmp_dst; // get a one line image.

    detector.detect( row_img, keypoints);

    dextract.compute( row_img, keypoints, descriptors_1);

    training_mat.push_back(descriptors_1);

    labels.at< float >(count_2, 0) = (count_2<nb_cars)?1:-1; // 1 for car, -1 otherwise*/

    ++count_2;
}

Error :

This code is implemented with the help of pseudo code given by @Mathieu Barnachon here

and how do i know that now my data is trained according to my need and now i can implement on my real time app to detect objects from video

@SR how i know where i am doing mistake its runtime logical error , if i knw where i did mistake than why i post the question here , and i post the small peace of code here and its clear from the error than i post the second part and so on ? i think i did the coding on my own and i am here for help not for coding , thats why i mention the error here , not demanding for code , and your point of view on long question is waste of time here , so than we can only question here if it is of one line ? i think my question is short and clear , i just want to know the error and that's it

Below is my code , which is running fine but after a long processing it show me the run time error

//FlannBasedMatcher matcher;
std::vector< DMatch > matches;
double max_dist = 0; 
double min_dist = 100;

Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("FlannBased");
Ptr<DescriptorExtractor> extractor = new SurfDescriptorExtractor();

//SurfDescriptorExtractor extractor;
SurfFeatureDetector detector(500);
std::vector<KeyPoint> keypoints;
int dictionarySize = 1500;
TermCriteria tc(CV_TERMCRIT_ITER, 10, 0.001);
int retries = 1;
int flags = KMEANS_PP_CENTERS;
BOWKMeansTrainer bow(dictionarySize, tc, retries, flags);
BOWImgDescriptorExtractor dextract(extractor,matcher);
// Initialize your training set.
cv::Mat training_mat(num_img,image_area,CV_32FC1);
cv::Mat labels(num_img,1,CV_32FC1);
cv::Mat tmp_dst( 500, 450, CV_8UC1 ); // to the right size for resize    
std::vector<string> all_names;

all_names.assign(files.begin(),files.end());
all_names.insert(all_names.end(), files_no.begin(), files_no.end());

// Load image and add them to the training set
int count = 0;
vector<string>::const_iterator i;
string Dir;
for (i = all_names.begin(); i != all_names.end(); ++i)
{
    Dir=( (count < files.size() ) ? YourImagesDirectory : YourImagesDirectory_2);

    tmp_img = cv::imread( Dir +*i, 0 );

    resize( tmp_img, tmp_dst, tmp_dst.size() );

    Mat row_img = tmp_dst;

    detector.detect( row_img, keypoints);

    extractor->compute( row_img, keypoints, descriptors_1);

    bow.add(descriptors_1);

    ++count;
}

int count_2=0;
vector<string>::const_iterator k;
Mat vocabulary = bow.cluster();
dextract.setVocabulary(vocabulary);
for (k = all_names.begin(); k != all_names.end(); ++k)
{
    Dir=( (count_2 < files.size() ) ? YourImagesDirectory : YourImagesDirectory_2);

    tmp_img = cv::imread( Dir +*k, 0 );

    resize( tmp_img, tmp_dst, tmp_dst.size() );

    Mat row_img = tmp_dst; // get a one line image.
tmp_dst;

    detector.detect( row_img, keypoints);

    dextract.compute( row_img, keypoints, descriptors_1);

    training_mat.push_back(descriptors_1);

    labels.at< float >(count_2, 0) = (count_2<nb_cars)?1:-1; // 1 for car, -1 otherwise*/

    ++count_2;
}

Error :

This code is implemented with the help of pseudo code given by @Mathieu Barnachon here

and how do i know that now my data is trained according to my need and now i can implement on my real time app to detect objects from video

@SR how i know where i am doing mistake its runtime logical error , if i knw where i did mistake than why i post the question here , and i post the small peace of code here and its clear from the error than i post the second part and so on ? i think i did the coding on my own and i am here for help not for coding , thats why i mention the error here , not demanding for code , and your point of view on long question is waste of time here , so than we can only question here if it is of one line ? i think my question is short and clear , i just want to know the error and that's it

Below is my code , which is running fine but after a long processing it show me the run time error

//FlannBasedMatcher matcher;
std::vector< DMatch > matches;
double max_dist = 0; 
double min_dist = 100;

Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("FlannBased");
Ptr<DescriptorExtractor> extractor = new SurfDescriptorExtractor();

//SurfDescriptorExtractor extractor;
SurfFeatureDetector detector(500);
std::vector<KeyPoint> keypoints;
int dictionarySize = 1500;
TermCriteria tc(CV_TERMCRIT_ITER, 10, 0.001);
int retries = 1;
int flags = KMEANS_PP_CENTERS;
BOWKMeansTrainer bow(dictionarySize, tc, retries, flags);
BOWImgDescriptorExtractor dextract(extractor,matcher);
// Initialize your training set.
cv::Mat training_mat(num_img,image_area,CV_32FC1);
cv::Mat labels(num_img,1,CV_32FC1);
cv::Mat tmp_dst( 500, 450, CV_8UC1 ); // to the right size for resize    
std::vector<string> all_names;

all_names.assign(files.begin(),files.end());
all_names.insert(all_names.end(), files_no.begin(), files_no.end());

// Load image and add them to the training set
int count = 0;
vector<string>::const_iterator i;
string Dir;
for (i = all_names.begin(); i != all_names.end(); ++i)
{
    Dir=( (count < files.size() ) ? YourImagesDirectory : YourImagesDirectory_2);

    tmp_img = cv::imread( Dir +*i, 0 );

    resize( tmp_img, tmp_dst, tmp_dst.size() );

    Mat row_img = tmp_dst;

    detector.detect( row_img, keypoints);

    extractor->compute( row_img, keypoints, descriptors_1);

    bow.add(descriptors_1);

    ++count;
}

int count_2=0;
vector<string>::const_iterator k;
Mat vocabulary = bow.cluster();
dextract.setVocabulary(vocabulary);
for (k = all_names.begin(); k != all_names.end(); ++k)
{
    Dir=( (count_2 < files.size() ) ? YourImagesDirectory : YourImagesDirectory_2);

    tmp_img = cv::imread( Dir +*k, 0 );

    resize( tmp_img, tmp_dst, tmp_dst.size() );

    Mat row_img = tmp_dst;

    detector.detect( row_img, keypoints);

    dextract.compute( row_img, keypoints, descriptors_1);

    training_mat.push_back(descriptors_1);

    labels.at< float >(count_2, 0) = (count_2<nb_cars)?1:-1; // 1 for car, -1 otherwise*/

    ++count_2;
}

Error :

This code is implemented with the help of pseudo code given by @Mathieu Barnachon here

and how do i know that now my data is trained according to my need and now i can implement on my real time app to detect objects from video